'''simple docstring'''





from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_:	Optional[Any]    =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:	List[Any]    ={
    'deepmind/language-perceiver': 'https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}




class 			__A (					UpperCamelCase__      ):
    a__			:		List[Any]			  =    """perceiver"""



    def __init__(self       :       Optional[int]  ,     __a       :       Tuple=256  ,     __a       :       Optional[Any]=1280  ,     __a       :       Optional[int]=768  ,     __a       :       Any=1  ,     __a       :       List[str]=26  ,     __a       :       Dict=8  ,     __a       :       List[Any]=8  ,     __a       :       Tuple=None  ,     __a       :       List[str]=None  ,     __a       :       Optional[int]="kv"  ,     __a       :       Union[str, Any]=1  ,     __a       :       List[str]=1  ,     __a       :       List[Any]="gelu"  ,     __a       :       List[str]=0.1  ,     __a       :       str=0.02  ,     __a       :       List[str]=1E-12  ,     __a       :       Optional[int]=True  ,     __a       :       Tuple=262  ,     __a       :       Dict=2048  ,     __a       :       int=56  ,     __a       :       Optional[int]=[368, 496]  ,     __a       :       Any=16  ,     __a       :       Optional[Any]=1920  ,     __a       :       Any=16  ,     __a       :       str=[1, 16, 224, 224]  ,     **__a       :       Any  ,     ):
         super().__init__(**__a					)

         UpperCAmelCase_			       =  num_latents
         UpperCAmelCase_			       =  d_latents
         UpperCAmelCase_			       =  d_model
         UpperCAmelCase_			       =  num_blocks
         UpperCAmelCase_			       =  num_self_attends_per_block
         UpperCAmelCase_			       =  num_self_attention_heads
         UpperCAmelCase_			       =  num_cross_attention_heads
         UpperCAmelCase_			       =  qk_channels
         UpperCAmelCase_			       =  v_channels
         UpperCAmelCase_			       =  cross_attention_shape_for_attention
         UpperCAmelCase_			       =  self_attention_widening_factor
         UpperCAmelCase_			       =  cross_attention_widening_factor
         UpperCAmelCase_			       =  hidden_act
         UpperCAmelCase_			       =  attention_probs_dropout_prob
         UpperCAmelCase_			       =  initializer_range
         UpperCAmelCase_			       =  layer_norm_eps
         UpperCAmelCase_			       =  use_query_residual
         # masked language modeling attributes
         UpperCAmelCase_			       =  vocab_size
         UpperCAmelCase_			       =  max_position_embeddings
         # image classification attributes
         UpperCAmelCase_			       =  image_size
         # flow attributes
         UpperCAmelCase_			       =  train_size
         # multimodal autoencoding attributes
         UpperCAmelCase_			       =  num_frames
         UpperCAmelCase_			       =  audio_samples_per_frame
         UpperCAmelCase_			       =  samples_per_patch
         UpperCAmelCase_			       =  output_shape




class 			__A (					UpperCamelCase__      ):



    @property
    def    _lowercase		(self       :       Dict					):
         if self.task == "multiple-choice":
              UpperCAmelCase_			       =  {0: "batch", 1: "choice", 2: "sequence"}
         else:
              UpperCAmelCase_			       =  {0: "batch", 1: "sequence"}
         return OrderedDict(
             [
                 ("inputs", dynamic_axis),
                 ("attention_mask", dynamic_axis),
             ]					)



    @property
    def    _lowercase		(self       :       Optional[Any]					):
         return 1E-4






    def    _lowercase		(self       :       Union[str, Any]  ,     __a       :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]  ,     __a       :       int = -1  ,     __a       :       int = -1  ,     __a       :       int = -1  ,     __a       :       bool = False  ,     __a       :       Optional[TensorType] = None  ,     __a       :       int = 3  ,     __a       :       int = 40  ,     __a       :       int = 40  ,     ):
         # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

         if isinstance(__a  ,     __a					):
              # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
              UpperCAmelCase_			       =  compute_effective_axis_dimension(
                  __a  ,     fixed_dimension=OnnxConfig.default_fixed_batch  ,     num_token_to_add=0					)
              # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
              UpperCAmelCase_			       =  preprocessor.num_special_tokens_to_add(__a					)
              UpperCAmelCase_			       =  compute_effective_axis_dimension(
                  __a  ,     fixed_dimension=OnnxConfig.default_fixed_sequence  ,     num_token_to_add=__a					)
              # Generate dummy inputs according to compute batch and sequence
              UpperCAmelCase_			       =  [" ".join(["a"]					) * seq_length] * batch_size
              UpperCAmelCase_			       =  dict(preprocessor(__a  ,     return_tensors=__a					)					)
              UpperCAmelCase_			       =  inputs.pop("input_ids"					)
              return inputs
         elif isinstance(__a  ,     __a					) and preprocessor.model_input_names[0] == "pixel_values":
              # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
              UpperCAmelCase_			       =  compute_effective_axis_dimension(__a  ,     fixed_dimension=OnnxConfig.default_fixed_batch					)
              UpperCAmelCase_			       =  self._generate_dummy_images(__a  ,     __a  ,     __a  ,     __a					)
              UpperCAmelCase_			       =  dict(preprocessor(images=__a  ,     return_tensors=__a					)					)
              UpperCAmelCase_			       =  inputs.pop("pixel_values"					)
              return inputs
         else:
              raise ValueError(
                  "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."					)





"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a				=logging.get_logger(__name__)

a				={
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  str    				=			'''deta'''
				A__ :  List[str]    				=			{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}

				def __init__( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=900      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=6      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=8      ,					_lowerCamelCase=6      ,					_lowerCamelCase=1024      ,					_lowerCamelCase=8      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase="relu"      ,					_lowerCamelCase=256      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0_2      ,					_lowerCamelCase=1.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase=False      ,					_lowerCamelCase="sine"      ,					_lowerCamelCase=5      ,					_lowerCamelCase=4      ,					_lowerCamelCase=4      ,					_lowerCamelCase=True      ,					_lowerCamelCase=300      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=1      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.2_5      ,					**_lowerCamelCase      ,					):
										if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
																lowerCamelCase__   =CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] )
										else:
																if isinstance(_lowerCamelCase      ,					_lowerCamelCase ):
																						lowerCamelCase__   =backbone_config.pop("model_type" )
																						lowerCamelCase__   =CONFIG_MAPPING[backbone_model_type]
																						lowerCamelCase__   =config_class.from_dict(_lowerCamelCase )

										lowerCamelCase__   =backbone_config
										lowerCamelCase__   =num_queries
										lowerCamelCase__   =max_position_embeddings
										lowerCamelCase__   =d_model
										lowerCamelCase__   =encoder_ffn_dim
										lowerCamelCase__   =encoder_layers
										lowerCamelCase__   =encoder_attention_heads
										lowerCamelCase__   =decoder_ffn_dim
										lowerCamelCase__   =decoder_layers
										lowerCamelCase__   =decoder_attention_heads
										lowerCamelCase__   =dropout
										lowerCamelCase__   =attention_dropout
										lowerCamelCase__   =activation_dropout
										lowerCamelCase__   =activation_function
										lowerCamelCase__   =init_std
										lowerCamelCase__   =init_xavier_std
										lowerCamelCase__   =encoder_layerdrop
										lowerCamelCase__   =auxiliary_loss
										lowerCamelCase__   =position_embedding_type
										# deformable attributes
										lowerCamelCase__   =num_feature_levels
										lowerCamelCase__   =encoder_n_points
										lowerCamelCase__   =decoder_n_points
										lowerCamelCase__   =two_stage
										lowerCamelCase__   =two_stage_num_proposals
										lowerCamelCase__   =with_box_refine
										lowerCamelCase__   =assign_first_stage
										if two_stage is True and with_box_refine is False:
																raise ValueError("If two_stage is True, with_box_refine must be True." )
										# Hungarian matcher
										lowerCamelCase__   =class_cost
										lowerCamelCase__   =bbox_cost
										lowerCamelCase__   =giou_cost
										# Loss coefficients
										lowerCamelCase__   =mask_loss_coefficient
										lowerCamelCase__   =dice_loss_coefficient
										lowerCamelCase__   =bbox_loss_coefficient
										lowerCamelCase__   =giou_loss_coefficient
										lowerCamelCase__   =eos_coefficient
										lowerCamelCase__   =focal_alpha
										super().__init__(is_encoder_decoder=_lowerCamelCase      ,					**_lowerCamelCase )

				@property
				def 						_a      ( self ):
										return self.encoder_attention_heads

				@property
				def 						_a      ( self ):
										return self.d_model






				def 						_a      ( self ):
										lowerCamelCase__   =copy.deepcopy(self.__dict__ )
										lowerCamelCase__   =self.backbone_config.to_dict()
										lowerCamelCase__   =self.__class__.model_type
										return output


